-
Notifications
You must be signed in to change notification settings - Fork 7
/
fsl_trainer.py
358 lines (312 loc) · 15.8 KB
/
fsl_trainer.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
import time
import os.path as osp
import numpy as np
from copy import deepcopy
import torch
import torch.nn.functional as F
from model.trainer.base import Trainer
from model.trainer.helpers import (
get_dataloader, prepare_model, prepare_optimizer, get_cross_shot_dataloader, get_class_dataloader
)
from model.utils import (
pprint, ensure_path,
Averager, Timer, count_acc,
compute_confidence_interval,
)
from tensorboardX import SummaryWriter
from tqdm import tqdm
class FSLTrainer(Trainer):
def __init__(self, args):
super().__init__(args)
self.train_loader, self.val_loader, self.test_loader = get_dataloader(args)
self.model = prepare_model(args)
self.optimizer, self.lr_scheduler = prepare_optimizer(self.model, args)
# save running statistics
running_dict = {}
for e in self.model.encoder.state_dict():
if 'running' in e:
key_name = '.'.join(e.split('.')[:-1])
if key_name in running_dict:
continue
else:
running_dict[key_name] = {}
# find the position of BN modules
component = self.model.encoder
for att in key_name.split('.'):
if att.isdigit():
component = component[int(att)]
else:
component = getattr(component, att)
running_dict[key_name]['mean'] = component.running_mean
running_dict[key_name]['var'] = component.running_var
self.running_dict = running_dict
# compute PCA given PCA-Noise
if args.model_class == 'MAMLNoise' and args.noise_type == 'PCA':
# get pre-trained features
if osp.exists(osp.join(*args.para_init.split('/')[:-1], 'PCAStats-{}.dat'.format(args.backbone_class))):
PCAStats = torch.load(osp.join(*args.para_init.split('/')[:-1], 'PCAStats-{}.dat'.format(args.backbone_class)))
self.model.PCAStats = PCAStats
else:
self.class_loader = get_class_dataloader(args)
self.model.eval()
PCAStats = {}
embedding_list = []
for batch in tqdm(self.class_loader, desc='Get Embeddings', ncols=50):
if torch.cuda.is_available():
c_data, c_label = batch[0].cuda(), batch[-1].cuda()
else:
c_data, c_label = batch[0], batch[-1]
unique_c_label = torch.unique(c_label)
assert(unique_c_label.shape[0] == 1)
c_label = unique_c_label.item()
# split the data in to shots and add them to the corresponding queue
with torch.no_grad():
inst_emb = []
for j in range(int(np.ceil(c_data.shape[0] / 128))):
inst_emb.append(self.model.encoder(c_data[j*128:min((j+1)*128, c_data.shape[0]), :], embedding = True))
inst_emb = torch.cat(inst_emb)
embedding_list.append(inst_emb.cpu())
# compute PCA
whole_embedding = np.concatenate(embedding_list)
from sklearn.decomposition import PCA
# np.where(np.cumsum(pca.explained_variance_ratio_) > 0.9)
pca = PCA(n_components=60)
pca.fit(whole_embedding)
PCAStats['s_values'] = torch.Tensor(pca.singular_values_)
PCAStats['s_vectors'] = torch.Tensor(pca.components_)
PCAStats['mean'] = torch.Tensor(pca.mean_)
if torch.cuda.is_available():
PCAStats['s_values'] = PCAStats['s_values'].cuda()
PCAStats['s_vectors'] = PCAStats['s_vectors'].cuda()
PCAStats['mean'] = PCAStats['mean'].cuda()
self.model.PCAStats = PCAStats
torch.save(PCAStats, osp.join(*args.para_init.split('/')[:-1], 'PCAStats-{}.dat'.format(args.backbone_class)))
self.model.train()
def prepare_label(self):
# prepare one-hot label
args = self.args
label = torch.arange(args.way, dtype=torch.int16).repeat(args.query)
label = label.type(torch.LongTensor)
if torch.cuda.is_available():
label = label.cuda()
return label
def train(self):
args = self.args
self.model.train()
if self.args.fix_BN:
self.model.encoder.eval()
# start FSL training
label = self.prepare_label()
for epoch in range(1, args.max_epoch + 1):
# initialize the repo with embeddings
self.train_epoch += 1
self.model.train()
if self.args.fix_BN:
self.model.encoder.eval()
tl1, tl2, ta = Averager(), Averager(), Averager()
start_tm = time.time()
self.model.zero_grad()
for batch in self.train_loader:
self.train_step += 1
data, gt_label = batch
if torch.cuda.is_available():
data = data.cuda()
gt_label = gt_label[:args.way] # get the ground-truth label of the current episode
data_tm = time.time()
self.dt.add(data_tm - start_tm)
support = data[:args.way * args.shot]
query = data[args.way * args.shot:]
logits = self.model(support, query)
loss = F.cross_entropy(logits, label)
tl2.add(loss.item())
forward_tm = time.time()
self.ft.add(forward_tm - data_tm)
acc = count_acc(logits, label)
tl1.add(loss.item())
ta.add(acc)
loss.backward()
backward_tm = time.time()
self.bt.add(backward_tm - forward_tm)
self.optimizer.step()
optimizer_tm = time.time()
self.ot.add(optimizer_tm - backward_tm)
self.model.zero_grad()
self.try_logging(tl1, tl2, ta)
# refresh start_tm
start_tm = time.time()
self.lr_scheduler.step()
print('LOG: Epoch-{}: Train Acc-{}'.format(epoch, acc))
self.try_evaluate(epoch)
print('ETA:{}/{}'.format(
self.timer.measure(),
self.timer.measure(self.train_epoch / args.max_epoch))
)
torch.save(self.trlog, osp.join(args.save_path, 'trlog'))
self.save_model('epoch-last')
def evaluate(self, data_loader):
# restore model args
args = self.args
args.old_way, args.old_shot, args.old_query = args.way, args.shot, args.query
args.way, args.shot, args.query = args.eval_way, args.eval_shot, args.eval_query
# evaluation mode
self.model.eval()
self.model.encoder.is_training = True
# record the runing mean and variance before validation
for e in self.running_dict:
self.running_dict[e]['mean_copy'] = deepcopy(self.running_dict[e]['mean'])
self.running_dict[e]['var_copy'] = deepcopy(self.running_dict[e]['var'])
record = np.zeros((args.num_eval_episodes, 2)) # loss and acc
label = torch.arange(args.eval_way, dtype=torch.int16).repeat(args.eval_query)
label = label.type(torch.LongTensor)
if torch.cuda.is_available():
label = label.cuda()
print('best epoch {}, best val acc={:.4f} + {:.4f}'.format(
self.trlog['max_acc_epoch'],
self.trlog['max_acc'],
self.trlog['max_acc_interval']))
for i, batch in enumerate(data_loader, 1):
if torch.cuda.is_available():
data = batch[0].cuda()
else:
data = batch[0]
support = data[:args.eval_way * args.eval_shot]
query = data[args.eval_way * args.eval_shot:]
logits = self.model.forward_eval(support, query)
for e in self.running_dict:
self.running_dict[e]['mean'] = deepcopy(self.running_dict[e]['mean_copy'])
self.running_dict[e]['var'] = deepcopy(self.running_dict[e]['mean_copy'])
loss = F.cross_entropy(logits, label)
acc = count_acc(logits, label)
record[i-1, 0] = loss.item()
record[i-1, 1] = acc
del data, support, query, logits, loss
torch.cuda.empty_cache()
assert(i == record.shape[0])
vl, _ = compute_confidence_interval(record[:,0])
va, vap = compute_confidence_interval(record[:,1])
# train mode
self.model.train()
if self.args.fix_BN:
self.model.encoder.eval()
self.model.encoder_repo.eval()
args.way, args.shot, args.query = args.old_way, args.old_shot, args.old_query
return vl, va, vap
def evaluate_test(self):
# restore model args
args = self.args
args.old_way, args.old_shot, args.old_query = args.way, args.shot, args.query
args.way, args.shot, args.query = args.eval_way, args.eval_shot, args.eval_query
# evaluation mode
self.model.load_state_dict(torch.load(osp.join(self.args.save_path, 'max_acc.pth'))['params'])
self.model.eval()
self.model.encoder.is_training = True
# record the runing mean and variance before validation
for e in self.running_dict:
self.running_dict[e]['mean_copy'] = deepcopy(self.running_dict[e]['mean'])
self.running_dict[e]['var_copy'] = deepcopy(self.running_dict[e]['var'])
record = np.zeros((10000, 2)) # loss and acc
label = torch.arange(args.eval_way, dtype=torch.int16).repeat(args.eval_query)
label = label.type(torch.LongTensor)
if torch.cuda.is_available():
label = label.cuda()
print('best epoch {}, best val acc={:.4f} + {:.4f}'.format(
self.trlog['max_acc_epoch'],
self.trlog['max_acc'],
self.trlog['max_acc_interval']))
for i, batch in tqdm(enumerate(self.test_loader, 1)):
if torch.cuda.is_available():
data = batch[0].cuda()
else:
data = batch[0]
support = data[:args.eval_way * args.eval_shot]
query = data[args.eval_way * args.eval_shot:]
logits = self.model.forward_eval(support, query)
for e in self.running_dict:
self.running_dict[e]['mean'] = deepcopy(self.running_dict[e]['mean_copy'])
self.running_dict[e]['var'] = deepcopy(self.running_dict[e]['mean_copy'])
loss = F.cross_entropy(logits, label)
acc = count_acc(logits, label)
record[i-1, 0] = loss.item()
record[i-1, 1] = acc
del data, support, query, logits, loss
torch.cuda.empty_cache()
assert(i == record.shape[0])
vl, _ = compute_confidence_interval(record[:,0])
va, vap = compute_confidence_interval(record[:,1])
self.trlog['test_acc'] = va
self.trlog['test_acc_interval'] = vap
self.trlog['test_loss'] = vl
print('best epoch {}, best val acc={:.4f} + {:.4f}\n'.format(
self.trlog['max_acc_epoch'],
self.trlog['max_acc'],
self.trlog['max_acc_interval']))
print('Test acc={:.4f} + {:.4f}\n'.format(
self.trlog['test_acc'],
self.trlog['test_acc_interval']))
args.way, args.shot, args.query = args.old_way, args.old_shot, args.old_query
return vl, va, vap
def evaluate_test_cross_shot(self):
# restore model args
args = self.args
# evaluation mode
self.model.load_state_dict(torch.load(osp.join(self.args.save_path, 'max_acc.pth'))['params'])
self.model.eval()
self.model.encoder.is_training = True
# record the runing mean and variance before validation
for e in self.running_dict:
self.running_dict[e]['mean_copy'] = deepcopy(self.running_dict[e]['mean'])
self.running_dict[e]['var_copy'] = deepcopy(self.running_dict[e]['var'])
# num_shots = [1, 5, 10, 20, 30, 50]
num_shots = [1, 5]
record = np.zeros((10000, len(num_shots))) # loss and acc
label = torch.arange(args.eval_way, dtype=torch.int16).repeat(args.eval_query)
label = label.type(torch.LongTensor)
if torch.cuda.is_available():
label = label.cuda()
print('best epoch {}, best val acc={:.4f} + {:.4f}'.format(
self.trlog['max_acc_epoch'],
self.trlog['max_acc'],
self.trlog['max_acc_interval']))
for s_index, shot in enumerate(num_shots):
test_loader = get_cross_shot_dataloader(args, shot)
args.eval_shot = shot
args.old_way, args.old_shot, args.old_query = args.way, args.shot, args.query
args.way, args.shot, args.query = args.eval_way, args.eval_shot, args.eval_query
for i, batch in tqdm(enumerate(test_loader, 1)):
if torch.cuda.is_available():
data = batch[0].cuda()
else:
data = batch[0]
support = data[:args.eval_way * shot]
query = data[args.eval_way * shot:]
logits = self.model.forward_eval(support, query)
for e in self.running_dict:
self.running_dict[e]['mean'] = deepcopy(self.running_dict[e]['mean_copy'])
self.running_dict[e]['var'] = deepcopy(self.running_dict[e]['mean_copy'])
loss = F.cross_entropy(logits, label)
acc = count_acc(logits, label)
record[i-1, s_index] = acc
del data, support, query, logits, loss
torch.cuda.empty_cache()
assert(i == record.shape[0])
va, vap = compute_confidence_interval(record[:,s_index])
print('Shot {} Test acc={:.4f} + {:.4f}\n'.format(shot, va, vap))
args.way, args.shot, args.query = args.old_way, args.old_shot, args.old_query
with open(osp.join(self.args.save_path, '{}+{}-CrossShot'.format(va, vap)), 'w') as f:
f.write('best epoch {}, best val acc={:.4f} + {:.4f}\n'.format(
self.trlog['max_acc_epoch'],
self.trlog['max_acc'],
self.trlog['max_acc_interval']))
for s_index, shot in enumerate(num_shots):
va, vap = compute_confidence_interval(record[:,s_index])
f.write('Shot {} Test acc={:.4f} + {:.4f}\n'.format(shot, va, vap))
def final_record(self):
# save the best performance in a txt file
with open(osp.join(self.args.save_path, '{}+{}'.format(self.trlog['test_acc'], self.trlog['test_acc_interval'])), 'w') as f:
f.write('best epoch {}, best val acc={:.4f} + {:.4f}\n'.format(
self.trlog['max_acc_epoch'],
self.trlog['max_acc'],
self.trlog['max_acc_interval']))
f.write('Test acc={:.4f} + {:.4f}\n'.format(
self.trlog['test_acc'],
self.trlog['test_acc_interval']))